Information processing system and information processing method

ABSTRACT

An information processing system 100 includes a plurality of computers 220 having a graph structure as a model, the graph structure being formed by a plurality of vertices corresponding to events as objects of analysis and an edge connecting the corresponding vertices to each other according to relation between the corresponding events, the plurality of computers 220 corresponding to the respective vertices and being connected to each other so as to be able to send and receive data in correspondence with the edge. Each of the computers calculates the transition probability of identifier data between the computer and the computer corresponding to the adjacent vertex connected by the edge, by a predetermined algorithm based on the numbers of pieces of identifier data retained by the mutual computers, and updates the numbers of pieces of identifier data retained by the mutual computers according to a result of the calculation.

TECHNICAL FIELD

The present invention relates to an information processing system and aninformation processing method that perform graph processing.

BACKGROUND ART

A technology is drawing attention in which technology, in order todesign and operate a social infrastructure, a city, or the likeefficiently, data distributed in the real world or a cyberspace isprocessed, the state of the social infrastructure or the like isanalyzed and predicted, and elements constituting a society arecontrolled.

The above-described distributed data is constituted of environmentsensing data such as temperature, humidity, or the like, log data on amachine such as an automobile or the like, log data on a human or anorganization, such as that of email, SNS, or the like. In addition,details of the processing of such distributed data include clusteringprocessing that classifies the data and adds labels or indexes, machinelearning processing, and control processing that optimally arrangeselements (humans, things, information, and the like) constituting asociety. A result of the processing of the distributed data, whichresult is obtained by these pieces of processing, is spread out todistributed users or control objects. The users or the control objectsfor example determine moving means and a traveling direction ordetermine a control parameter according to the processing result.

The following technology has conventionally been proposed as such atechnology. Specifically, the technology analyzes and predicts orcontrols a social infrastructure by integrating physically distributedsensing data into a computer system via communicating means such as theInternet or the like, processing the data, and spreading out a result ofthe processing to the control object (see Patent Document 1).

PRIOR ART DOCUMENT Patent Document

Patent Document 1: US 2013/0151536

SUMMARY OF THE INVENTION Problem to be Solved by the Invention

However, the conventional technology (Personalized Pagerank Algorithm)as described above requires synchronization between calculating entitieswhen parallel calculation is performed, and cannot perform processingunless distributed data is integrated into a parallel computer. Further,a calculation result obtained by the calculation needs to be spread outto each distributed control object. Hence, the conventional technologycannot process very large scale data difficult to collect at one place,data that takes time to integrate and spread out and which is alsoupdated momently, or the like.

It is accordingly an object of the present invention to provide atechnology that makes it possible to perform efficient calculation onlarge-scale data that cannot be collected at one place or data updatedmomently.

Means for Solving the Problems

An information processing system according to the present invention forsolving the above-described problem includes: a plurality of computershaving a graph structure as a model, the graph structure being formed bya plurality of vertices corresponding to events as objects of analysisand an edge connecting the corresponding vertices to each otheraccording to relation between the corresponding events, the plurality ofcomputers corresponding to the respective vertices and being connectedto each other so as to be able to send and receive data incorrespondence with the edge; a storage device configured to retainidentifier data having an attribute indicating one or more states forthe events of the vertices, the identifier data being accumulated ineach of the computers; and a display device configured to display anumber of pieces of the identifier data retained in the storage devicein relation to each of the vertices as a space distribution diagrambased on distribution of the vertices; each of the computers calculatingtransition probability of identifier data between the computer and thecomputer corresponding to the adjacent vertex connected by the edge, bya predetermined algorithm based on the numbers of pieces of theidentifier data retained by the mutual computers, and updating thenumbers of pieces of the identifier data retained by the mutualcomputers according to a result of the calculation.

In addition, an information processing system according to the presentinvention includes: a managing computer; computers installed onrespective racks within a warehouse; and portable terminals carried byrespective workers collecting baggage arranged in the racks, theportable terminals being able to access the computers; each of thecomputers retaining information on numbers of pieces of identifier dataof a plurality of kinds corresponding to predetermined events related tothe racks or the baggage arranged in the corresponding racks, andreceiving identifier data retained by each of the portable terminalswhen accessed by the portable terminal, transmitting identifier datadetermined on a basis of conditions of past changes in the identifierdata already retained by the computer to the portable terminal, andupdating the numbers of pieces of the identifier data of the pluralityof kinds, the identifier data of the plurality of kinds being retainedby the computer, on a basis of a subtracted number as a result of thetransmission and reception of the identifier data to and from theportable terminal, each of the portable terminals receiving theidentifier data from the computer, and updating identifier data retainedby the portable terminal itself on a basis of the received identifierdata, and each of the computers outputting, to the managing computer, aninstruction to move the rack on which the computer itself is installedto an arrangement destination associated with a kind corresponding to alargest number of the numbers of pieces of the identifier data of theplurality of kinds, the identifier data of the plurality of kinds beingretained by the computer itself, after passage of a predetermined timefrom the updating of the numbers of pieces of the identifier data.

In addition, an information processing system according to the presentinvention includes: a managing computer; and computers associated withrespective pieces of data within a data center; each of the computersretaining information on numbers of pieces of identifier data of aplurality of kinds corresponding to predetermined events related to thedata, and when accessed by programs using each of the pieces of data,receiving identifier data retained by the programs, giving the programsidentifier data determined on a basis of conditions of past changes inthe identifier data already retained by the computer, and updating thenumbers of pieces of the identifier data of the plurality of kinds, theidentifier data of the plurality of kinds being retained by thecomputer, on a basis of a subtracted number as a result of thetransmission and reception of the identifier data to and from theprograms, each of the programs receiving the identifier data from thecomputer, and updating identifier data retained by the program itself ona basis of the received identifier data, and each of the computersoutputting, to the managing computer, an instruction to move the datawith which the computer itself is associated to an arrangementdestination associated with a kind corresponding to a largest number ofthe numbers of pieces of the identifier data of the plurality of kinds,the identifier data of the plurality of kinds being retained by thecomputer itself, after passage of a predetermined time from the updatingof the numbers of pieces of the identifier data.

In addition, an information processing system according to the presentinvention includes: a plurality of terminals configured to transmit andreceive a message via a network; and a managing computer; each of theterminals retaining information on numbers of pieces of identifier dataof a plurality of kinds, when transmitting the message, transmitting, toanother terminal among the plurality of terminals, the message to whichidentifier data determined on a basis of conditions of past changes inthe numbers of pieces of identifier data already retained by theterminal itself is added, and updating the numbers of pieces of theidentifier data of the plurality of kinds, the identifier data of theplurality of kinds being retained by the terminal itself, by subtractinga number of pieces of the transmitted identifier data, when receivingthe message, updating the numbers of pieces of the identifier data ofthe plurality of kinds, the identifier data of the plurality of kindsbeing retained by the terminal itself, by adding a number of pieces ofidentifier data added to the received message, and transmitting, to themanaging computer, the numbers of pieces of the identifier data of theplurality of kinds, the identifier data of the plurality of kinds beingretained by the terminal itself, after passage of a predetermined timefrom the updating of the numbers of pieces of the identifier data, andthe managing computer receiving the numbers of pieces of the identifierdata of the plurality of kinds from each of the terminals, anddisplaying information indicating that terminals each having a largestnumber of pieces of identifier data belonging to a common kind are in asame group on a display terminal of the managing computer.

In addition, an information processing method according to the presentinvention includes: by a plurality of computers having a graph structureas a model, the graph structure being formed by a plurality of verticescorresponding to events as objects of analysis and an edge connectingthe corresponding vertices to each other according to relation betweenthe corresponding events, the plurality of computers corresponding tothe respective vertices, being connected to each other so as to be ableto send and receive data in correspondence with the edge, and retainingidentifier data having an attribute indicating one or more states forthe events of the vertices, calculating transition probability ofidentifier data between each of the computers and the computercorresponding to the adjacent vertex connected by the edge, by apredetermined algorithm based on numbers of pieces of the identifierdata retained by the mutual computers, and updating the numbers ofpieces of the identifier data retained by the mutual computers accordingto a result of the calculation; and displaying, on a display device, thenumber of pieces of the identifier data retained in relation to each ofthe vertices as a space distribution diagram based on distribution ofthe vertices.

Effect of the Invention

According to the present invention, it becomes possible to performefficient calculation on large-scale data that cannot be collected atone place or data updated momently.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing an example of a graph structure model as anobject of analysis in a first embodiment.

FIG. 2 is a conceptual diagram of the graph structure model as an objectof analysis in the first embodiment.

FIG. 3 is a diagram of a network configuration including an informationprocessing system in the first embodiment.

FIG. 4 is a flowchart illustrating a first procedure example in aninformation processing method according to the first embodiment.

FIG. 5 is a flowchart illustrating a second procedure example in theinformation processing method according to the first embodiment.

FIG. 6 is a flowchart illustrating a third procedure example in theinformation processing method according to the first embodiment.

FIG. 7 is a flowchart illustrating a fourth procedure example in theinformation processing method according to the first embodiment.

FIG. 8 is a diagram of assistance in explaining outlines of calculationorder in the first embodiment.

FIG. 9 is a diagram showing a more concrete calculation model example inthe first embodiment.

FIG. 10 is a diagram showing an example of configuration of a computerin an information processing system according to a second embodiment.

FIG. 11 is a diagram showing graph structure data in a case where edgesin a calculation model in the second embodiment have a weight.

FIG. 12 is a diagram showing a table of relation between the weights ofthe edges and delay times in the second embodiment.

FIG. 13 is a diagram showing an example of transition times ofinformation elements in the second embodiment.

FIG. 14 is a diagram showing a first example of a concept of obtaininggraph structure data based on activities in the real world in a thirdembodiment.

FIG. 15 is a diagram showing a second example of the concept ofobtaining the graph structure data based on the activities in the realworld in the third embodiment.

FIG. 16 is a diagram showing an example of configuration of aninformation processing system in the third embodiment.

FIG. 17 is a flowchart illustrating a first procedure example in aninformation processing method according to the third embodiment.

FIG. 18 is a flowchart illustrating a second procedure example in theinformation processing method according to the third embodiment.

FIG. 19 is a flowchart illustrating a third procedure example in theinformation processing method according to the third embodiment.

FIG. 20 is a flowchart illustrating a fourth procedure example in theinformation processing method according to the third embodiment.

FIG. 21 is a diagram showing an example of configuration of aninformation processing system in a fourth embodiment.

FIG. 22 is a diagram showing an example of a chart of access to datablocks by processes in the fourth embodiment.

FIG. 23 is a diagram showing an example of process relation in thefourth embodiment.

FIG. 24 is a diagram showing an outline of an information processingsystem in the fourth embodiment.

FIG. 25 is a diagram showing an example of a chart of access byprocesses in the fourth embodiment.

FIG. 26 is a diagram showing an example of processing on informationelement storage regions corresponding to data blocks in the fourthembodiment.

FIG. 27 is a diagram showing an example of temporal changes in thenumbers of information elements stored by the respective informationelement storage regions in the fourth embodiment.

FIG. 28 is a diagram showing an example of configuration of a warehousein a fifth embodiment.

FIG. 29 is a diagram showing an example of a concept of pickup work inthe fifth embodiment.

FIG. 30 is a diagram showing an example of a computer provided to a rackand a mobile terminal retained by a worker in the fifth embodiment.

FIG. 31 is a diagram showing an example of hardware configuration of thecomputer and the mobile terminal in the fifth embodiment.

FIG. 32 is a diagram showing a concept of a flow of an informationprocessing method in the fifth embodiment.

FIG. 33 is a diagram showing a table describing relation betweencomputers provided to respective racks and movement destinations in thefifth embodiment.

FIG. 34 is a diagram showing an example of a flowchart of a computer inthe fifth embodiment.

FIG. 35 is a diagram showing an example of a flowchart of calculationresult obtainment processing in the fifth embodiment.

FIG. 36 is a diagram showing an example of an effect of the fifthembodiment.

FIG. 37 is a diagram showing an example of a concept in a sixthembodiment.

FIG. 38 is a diagram showing an example of exchange of informationelements in the sixth embodiment.

FIG. 39 is a diagram showing an example of a concept of identifyingcommunities to which respective users belong from the numbers ofinformation elements of respective vertices in the sixth embodiment.

FIG. 40 is a diagram showing an example of a concept in a seventhembodiment.

FIG. 41 is a diagram showing a first example of a transition probabilitytable in an eighth embodiment.

FIG. 42 is a diagram showing a second example of the transitionprobability table in the eighth embodiment.

FIG. 43 is a diagram showing a third example of the transitionprobability table in the eighth embodiment.

MODES FOR CARRYING OUT THE INVENTION

Embodiments of the present invention will hereinafter be described indetail with reference to the drawings. The concept of a technical ideaof an information processing method in a present embodiment will firstbe described with problems of the conventional technology also takeninto account. The conventional technology can perform data analysis onlyafter all of target data is collected at one place. In a case wherelarge-scale data, or so-called big data, is an object of analysis, theconventional technology requires processing of efficiently collectingall pieces of data scattered over a very large area with immediacy,analyzing the data, and further sending a result of the analysis as aresponse to each object as an origin of data. It is particularlydifficult to apply the conventional technology to data updatedfrequently. Accordingly, in order to solve such a problem, theinformation processing method according to the present embodimentperforms autonomous distributed data analysis, that is, performsanalysis for distributed data on a piece-by-piece basis. This autonomousdistributed data analysis is a method in which each element(specifically a computer) managing distributed data performs apredetermined calculation on the data of the element itself and the dataof another adjacent element, and the elements as a whole perform adesired calculation.

Phenomena corresponding to the concept of the above-described autonomousdistribution are phenomena often observed in nature. For example,reaction-diffusion models in the field of biology are well known. In amodel related to the formation of a striped pattern of a zebra, forexample, among the reaction-diffusion models, it is considered that thestriped pattern of a zebra occurs when the diffusion of protein in eachcell is performed individually. The present embodiment illustrates atechnology applied to data analysis with the reaction-diffusion model ofsuch autonomous distribution replaced with conditions in which thediffusion of things, information, or the like is individually performedat various places and various elements.

First Embodiment

Description will first be made of conceptual operation of an informationprocessing system according to the present invention. FIG. 1 shows aconceptual diagram of a graph structure model (hereinafter a calculationmodel 10) as an object of analysis in the present information processingsystem. In this calculation model 10, calculation is performed bydiffusing information elements as information units through a graphstructure formed by vertices 110 to 114 and edges 120 connecting thesevertices to each other, and the number of information elements on eachof the vertices 110 to 114 is a solution. In addition, suppose that acalculation target in the present example is a classification problem.

Here, an information element (identifier) as a unit of information isdefined. An information element is data having a state variable. Thenumber of states in the present example is two (that is, a data capacityof one bit), and the respective states are a state u and a state v. FIG.1 illustrates information elements 130 in the state u and informationelements 131 in the state v. The information elements are diffused alongedges 120 (diffusion 140 of the information elements). A result ofcalculation can be obtained from the numbers of information elements oneach vertex. From a table 150 of the numbers of information elements oneach vertex (see FIG. 2), a vertex A (vertex 110), for example, has anumber of information elements (state u)=0 and a number of informationelements (state v)=2, and the number of information elements (state v)is a maximum. When the state of the information elements the number ofwhich is the maximum is made to correspond to a classification result,that is, supposing that the state u corresponds to a classificationresult A and that the state v corresponds to a classification result B,the vertex A belongs to the classification result B. When similarcalculation is performed at each vertex, all of the vertices can beclassified into either A or B.

Description will next be made of an example of configuration of aninformation processing system 100 in the present embodiment. FIG. 3 is adiagram showing an example of a network configuration including theinformation processing system 100 in the present embodiment. Theinformation processing system 100 shown in FIG. 3 includes one or morecomputers 220-1 to 220-4. These computers are connected to each other bya network 1. Incidentally, the computers will hereinafter be describedas computers 220 unless the computers are particularly distinguishedfrom each other.

These computers 220 include a CPU 221, a main storage device 222 formedby a volatile storage device such as a RAM or the like, a storage 223formed by an appropriate nonvolatile storage device such as a hard diskdrive or the like, an input-output device 224 such as a keyboard, amouse, a display, and the like, and a network I/F 225. In a computer 220including such a configuration, the CPU 221 implements necessaryfunctions, performs centralized control of the computer itself, andperforms various kinds of determinations, operations, and controlprocessing, by executing a program 226 retained in the main storagedevice 222. Hence, functions corresponding to the information processingmethod according to the present embodiment correspond to the functionsimplemented by the above-described computer 220 by executing the program226.

An actual procedure of the information processing method in the presentembodiment will next be described with reference to drawings while thecalculation model of FIG. 3 described above is described again. FIG. 4is a flowchart showing an example of a procedure in the informationprocessing method according to the present embodiment. In this case, infirst step 311, each of the computers 220-1 to 220-4 stores, in thestorage 223 of the computer itself or the like, data on correspondingvertices in the above-described graph structure model 10, which verticesare arranged in a divided manner for each computer. For example, thedata stored in the storage 223 by the computer 220-1 is stored in apredetermined data area 230-1 in the storage 223 as illustrated in FIG.3, for example. Here, the graph structure model 10 similar to that ofFIG. 1 is assumed, and the same reference characters are given.

The above-described data area 230-1 stores a vertex 110 as a vertex A, avertex 111 as a vertex B, and information on the connection destinationsof each vertex, that is, information indicating that the vertex 110 asthe vertex A is connected to a vertex 113 as a vertex D and that thevertex 111 as the vertex B is connected to the vertex 110 as the vertexA, a vertex 112 as a vertex C, and the vertex 113 as the vertex D.

Next, in step 312, each computer 220 initializes, to numbers determinedin advance, the numbers of information elements allocated to each vertexof all of the vertices included in the graph structure model 10 obtainedin the above-described step 311. For example, when the numbersdetermined in advance for the vertex 110 as the vertex A are zero forthe state u and two for the state v, the computer 220 sets the numbersof information elements assigned to the vertex A in the data retained inthe storage 223 such that the number of information elements (state u)is zero and the number of information elements (state v) is two.

After the above-described step 312, each computer 220 performs loopprocessing (step 313-1 to 313-2) a certain number of times. Within theloop processing, each computer 220 performs loop processing (step 314-1to 314-2) for all of the vertices whose data is obtained in theabove-described step 311, and performs information element receptionprocessing (step 315) and information element transmission processing(step 316). After completing the two pieces of loop processing (step 313and step 314) described above, each computer 220 performs calculationresult obtainment processing (step 317), and then ends the present flow.

Description will next be made of the reception processing (step 315),the transmission processing (step 316), and the calculation resultobtainment processing (step 317) in the above-described flow. Aflowchart of the reception processing (step 315) among these pieces ofprocessing is shown in FIG. 5. After starting the flow, each computer220 determines whether an information element is received from anothervertex (step 411). When the determination sentence is true (step 411:Y), the computer 220 updates the number of information elements whosedata is retained in the storage 223 (step 412). When the above-describeddetermination sentence is false (step 411: N), the computer 220 ends thepresent flow.

In addition, a flowchart of the transmission processing (step 316) isshown in FIG. 6. After starting the flow, each computer 220 obtains thenumbers of information elements on vertices connected by edges to avertex selected in the loop processing for the vertices described above(step 314) (the connected vertex will be referred to as an adjacentvertex) (step 512). Incidentally, the numbers of information elementsobtained here may be the numbers of information elements obtained in thepast.

The computer 220 then performs loop processing (step 513-1 to 513-2) forthe information elements on the vertex selected in the above-describedstep 314. The computer 220 calculates a transition probability for aninformation element selected in the above-described step 314 within theloop (step 514). An equation for calculating the transition probabilityis shown in the following asMathematical Equation 1:

                         (Mathemetical  Equation  1) $\begin{matrix}{P_{u} = {{{- \alpha_{a}}{f_{a}\left( {N_{u},N_{v}} \right)}} + {\alpha_{r}{f_{r}\left( {N_{u},N_{v}} \right)}} -}} \\{{\beta_{a}{\sum\limits_{\substack{j \in {{devices}\mspace{14mu}{within}} \\ a\mspace{20mu}{vicinity}\mspace{14mu}{range}}}\;{g_{a}\left( {N_{uj},N_{vj}} \right)}}} + {\beta_{r}{\sum\limits_{\substack{j \in {{devices}\mspace{14mu}{within}} \\ a\mspace{20mu}{vicinity}\mspace{14mu}{range}}}\;{g_{r}\left( {N_{uj},N_{vj}} \right)}}}} \\{= {{- {\alpha_{a}\left( {N_{u} - N_{u}^{2}} \right)}} + {\alpha_{r}\left( {N_{v} - N_{u}} \right)} -}} \\{{\beta_{a}{\sum\limits_{\substack{j \in {{devices}\mspace{14mu}{within}} \\ a\mspace{20mu}{vicinity}\mspace{14mu}{range}}}\;\frac{N_{uj}}{N_{dj}}}} + {\beta_{r}{\sum\limits_{\substack{j \in {{devices}\mspace{14mu}{within}} \\ a\mspace{20mu}{vicinity}\mspace{14mu}{range}}}\;\frac{N_{vj}}{N_{dj}}}}}\end{matrix}$where Ndj is the order of a vertex j. In addition, a left side Prepresents the transition probability, and Pu represents the transitionprobability of the information element in the state u. In addition, uand v denote different states of an information element. For example, inthe case of an information element assuming two states (one bit), u is astate=0, and v is a state=1. In addition, Nu and Nv denote a number ofinformation elements. For example, Nu is the number of informationelements in the state u, and Nv is the number of information elements inthe state v. In addition, E of a third term and a fourth term iscalculated for devices within a vicinity range. Nui and Nvj are thenumbers of information elements in the respective states of a device jwithin the vicinity range. In addition, f and g are functions, and a and(are positive constants.

Incidentally, the computer 220 may calculate the transition probabilityby a prediction based on a history of update of information elements inthe past. Suppose that in such a predicting method, the computer 220 ismade to perform learning by a predetermined neural network in advance byusing a result obtained by inputting a test pattern to theabove-described Mathematical Equation 1 as correct solution data, andusing the history of update of information elements in the past and thenumbers of information elements of the vertex as input data, and uses athus learned model for the calculation of the transition probability. Byusing the present model, the computer 220 can replace the numbers ofinformation elements of the adjacent vertices with the history of updateof information elements in the past.

Following the above-described step 514, the computer 220 in step 515compares the transition probability calculated in the above-describedstep 514 with a threshold value determined in advance. Recognizing as aresult that, when the threshold value is 0.5, for example, 0.5Transition Probability≤1 (step 515: Y), that is, that the probability ishigh, the computer 220 advances the processing to step 516. On the otherhand, when 0≤Transition Probability≤0.5, for example (step 515: N), thecomputer 220 recognizes that the transition probability is low, and thenends the processing (step 520).

The computer 220 in step 516 selects one vertex from the adjacentvertices whose numbers of information elements are obtained in step 512.A method for the selection in this case may be carried out in a randommanner, a round robin manner, or the like. Next, the computer 220 instep 517 transmits the data (state) of the information element from thenetwork I/F 125 to the adjacent vertex (computer corresponding to theadjacent vertex) selected in the above-described step 516. In addition,the computer 220 in step 518 updates the number of information elementsof the corresponding vertex (subtracts one from the number ofinformation elements because the information element is transmitted).

Next, an example of a detailed flow of the calculation result obtainmentprocessing (step 317) in the flow of FIG. 4 is shown in FIG. 7. Afterstarting the flow, the computer 220 in step 611 compares the numbers ofinformation elements in the respective states of each vertex, whichnumbers are retained in the storage 223 by the computer itself, witheach other, and selects the state that has a maximum number ofinformation elements. For example, in a case where the number of statesis two (u and v), and the number of information elements in the state uon a certain vertex is one and the number of information elements in thestate v on the vertex is two, the computer 220 selects the state v.

Next, the computer 220 in step 612 displays a result corresponding tothe state selected in the above-described step 611 on a predetermineddisplay device on the network 1 or the input-output device 224. Forexample, when the result corresponding to the state v is theclassification result B, it is determined that the vertex belongs to theclassification B (the state u corresponds to a community A). A similardetermination is made for the other vertices.

Here, as a feature of the present calculation model, the calculationresult does not depend on calculation order of the information elements.In the loop for vertices (step 314-1) and the loop for informationelements on a vertex (513-1) in the flow illustrated in FIG. 4 and FIG.6, the calculation order of the vertices and the information elements isfree. For example, the calculation result does not change between a casewhere a certain vertex A is processed and a vertex B is thereafterprocessed and a case where the vertex B is processed and thereafter thevertex A is processed. That is, the calculation order is free. FIG. 8shows outlines of the calculation order when the calculation model 10and the information processing system 100 of FIG. 3 are assumed. FIG. 8shows two flows that are different from each other in the calculationorder, and which will be described as processing order 1 and processingorder 2. In addition, of data (information element) movements resultingfrom transmission and reception between vertices, only movements relatedto the vertex D are shown.

In the processing order 1, the vertex D has edges with the vertex A andthe vertex B and a vertex E, and therefore movements of informationelements occur between the vertices. In the calculation order 1, thetransmission processing of the vertex A in the same cycle as that of thevertex D and the transmission processing of the vertex E in the samecycle as that of the vertex D are performed before the receptionprocessing of the vertex D. However, the transmission processing of thevertex B in the same cycle as that of the vertex D is not yet performed.Therefore, in the reception processing of the vertex D, the followingmovements occur: a movement 710 of an information element in the samecycle from the vertex A, a movement 712 of an information element in aprevious cycle from the vertex B, and a movement 711 of an informationelement in the same cycle from the vertex E. On the other hand, in theprocessing order 2, the transmission processing of the vertex A, thevertex B, and the vertex E is not performed before the receptionprocessing of the vertex D. Therefore, a movement 750 of an informationelement in a previous cycle from the vertex A, a movement 750 of aninformation element in the previous cycle from the vertex B, and amovement 750 of an information element in the previous cycle from thevertex E occur. Thus, timing of movement of information elements differsbetween the processing order 1 and the processing order 2. However,calculation results in both of the processing order 1 and the processingorder 2 converge to a same calculation result after processing oftransmission and reception of information elements is repeated for acertain time. The above indicates that, with the present calculationmodel and the configuration example shown in FIG. 3, the calculationresult does not change even when each computer performs calculationindependently at a time of parallel calculation. Hence, processing canbe performed even when the computers 220 are distributed over a widearea, and are unable to perform cooperation such as synchronization orthe like due to a problem of a delay of communicating means between thecomputers. It is therefore possible to solve a classification problemfor an object problem in which data is distributed over a wide area.

A more concrete example is shown in FIG. 9. The example shown in FIG. 9is a calculation model in which the number of vertices is 4096.Incidentally, FIG. 9 does not show edges because the edges make thefigure complicated. In FIG. 9, a region around the center of a circle isdense with vertices, and the density of vertices is lowered toward aperiphery. In addition, as for an undulating gap that traverses thecircle and which has no vertices, a distance between vertices around theperiphery of the circle is large than the gap around the center of thecircle. Therefore, when classification according to the threshold valueof distance, which is one of conventional statistical methods, isperformed, the undulating gap cannot be recognized, and theclassification cannot be performed correctly. On the other hand, themethod according to the present invention performs classification in amanner of autonomous distribution, and can therefore correctly recognizethe gap in the high-density region around the center and the gap in thelow-density region around the periphery. Suppose that in the calculationmodel shown in FIG. 9, the number of states of information elements istwo, and 4096×8 information elements (state u) are allocated to a vertexgroup 810-1 and 4096×8 information elements (state v) are allocated to avertex group 810-2 in the initialization of the numbers of informationelements. In addition, at each vertex in FIG. 9, thickness (colorthickness from white through gray to black) shown in the figure isvaried between a case where there are a large number of informationelements in the state u and a case where there are a large number ofinformation elements in the state v.

In a state 810 with a loop count t in FIG. 9, information elements areonly diffused through a part of an upper end of graph structure data anda part of a lower end of the graph structure data. However, in a state820 with a loop count t+n, the information elements are spreadthroughout. However, in the above-described state with the loop countt+n, classification accuracy is decreased in the vicinity of the gap inthe center of the graph structure data. On the other hand, neatclassification is performed in a state 830 with a loop count t+2n.

Second Embodiment

A case where each edge defined in the calculation model of the firstembodiment retains a weight coefficient will next be described as asecond embodiment. In the present second embodiment, delay processingbased on the weight coefficient of each edge is added to thetransmission processing shown in the first embodiment. The computer 220therefore has a delay device that performs the corresponding processing.

An example of configuration of a computer 910 in an informationprocessing system in this case is as shown in FIG. 10. The computer 910shown in this case corresponds to each of the computers 220-1 to 220-4shown in FIG. 3. Modules having the same functions are identified by thesame reference characters. The computer 910 in the second embodiment isnewly provided with a delay device 911 as compared with the computers220-1 to 220-4.

On the basis of this, graph structure data 920 in the case where edgesin the calculation model have a weight is shown in FIG. 11. Suppose thatthe graph structure data 920 includes a vertex A 950, a vertex B 951,and a vertex C 952, that an edge between the vertices A and B has aweight 960 whose value is one, and that an edge between the vertices Aand C has a weight 961 whose value is 10. When an information elementmoves from the vertex A 950 to the vertex B 951 in the graph structuredata 920, the computer 910 delays the transition of the informationelement by the delay device 911 according to the above-described weight960. FIG. 12 shows a table 940 defining relation between the weights ofsuch edges and delay times. The computer 910 controls the transitiontime of an information element. A transition time 930 of an informationelement as shown in FIG. 13, for example, when the information elementmoves from the vertex A 950 to the vertex B 951 is a delay time t=1(970) because the weight is one. Similarly, when an information elementmoves from the vertex A 950 to the vertex C 952, the weight is 10 (961),and therefore the delay time is t=10 (971). The relation between theweights and the delay times may be given by the table 940 in advance asin FIG. 12, or may be calculated by the computer 910 using apredetermined mathematical equation determined in advance or the like.

Third Embodiment

Description will next be made of an example of an information processingsystem having a function of automatically obtaining graph structure dataon the basis of activities in the real world at the time of step 311 inthe flow of FIG. 4 which flow is shown in the first embodiment, that is,the graph structure data obtainment processing. In a concept in thiscase, graph structure data as a calculation model is not calculated onthe information processing system, but activities in the real world areused as they are.

A concrete example thereof is shown in FIG. 14. When conversationbetween humans is assumed as activities in the real world, for example,in FIG. 14, graph structure data as a calculation model is structuredfrom a log of the conversation between the humans (log indicating whoand who had conversation how many times and for how long).

When a person A (1001) and a person B (1002) have conversation inactivities (1000) in the real world, for example, the computer 220 thathas obtained the recorded data generates an edge 1013 between a vertex A(1011) corresponding to the above-described person A and a vertex B(1012) corresponding to the person B as graph structure data 1010expressing the frequency and time of the conversation. The computer 220thus performs structuring 1030. Of course, the activities in the realworld are not limited to conversation between persons, but may beactivities between a thing and a thing (for example communicationbetween machines such as robots, automobiles, signals, or the like),activities between a person and a thing, activities between things via aperson (indirect communication between places or facilities via a personmaking rounds of a plurality of facilities or racks), interactionbetween SNS users in a virtual space (message communication, electronicmail, or the like), and the like. The computer 220 in this casestructures the activities in the real world into graph structure data,and then analyzes the graph structure data as in the processingillustrated in the first embodiment.

On the other hand, when information elements are made to accompany theactivities in the real world as input data when the graph structure datais structured, it is possible to perform calculation using the inputdata itself in structuring the graph structure data. FIG. 15 shows astate 1050 in which information elements are exchanged so as toaccompany the activities in the real world. When the person A and theperson B described above can retain information elements, and theinformation elements can be updated at a time of conversation betweenthe person A and the person B, the computer 220 performs calculation1070 using the activities in the real world, and obtains the numbers ofinformation elements retained in the real world, that is, between theperson A and the person B, as an analysis result 1060, which is a resultof the calculation. Concrete configurations corresponding to the presentconcept will be described in a third to a sixth embodiment.

A configuration of an information processing system in such a thirdembodiment will be described in the following. FIG. 16 shows an exampleof configuration of the information processing system 100 in the presentthird embodiment. The information processing system 100 illustrated hereincludes a device group 3001 and devices 3020 retained or implemented onvertices. Suppose in the present example that the information processingsystem having the real world as a processing object is described as anexample, that the device group 3001 is a collection of people in thereal world, and that each device is a smart device possessed by a personor the like. Of course, the device group is not limited to people, butmay be moving bodies such as vehicles or the like, smart phonesaccompanying machines, apparatuses such as embedded computers or thelike, or programs accompanying data. Suppose that a problem as an objectof analysis is a vertex group classification problem. In a case whereeach vertex is a person, the vertex group classification problem isapplied to detection of communities of certain groups, for example.

The device 3020 as a computer in this case includes a CPU 3021, a mainstorage device 3022 retaining a program 3026, a storage 3023, aninput-output device 3024, and a network I/F 3025. In addition, supposethat the device 3020 has a vicinity range 3030 recognized as a range inwhich communication can be performed. In the example of FIG. 16, thevicinity range 3030 is a circle having, as a center thereof, the device3010 accompanying a person or an apparatus, and having, as a radiusthereof, a value determined in advance. Such a vicinity range 3030 canbe calculated from a physical distance between devices. However, thevicinity range 3030 may be a reachable range of a radio wave transmittedby the network I/F 3025 or the like or a range in which the frequency ofcommunication between devices (that is, between vertices possessing thedevices), for example the number of times of exchange of email or thelike, is equal to or more than a certain threshold value.

In addition, the device 3020 can communicate within the vicinity range3030 using the network I/F 3025 or the like. In the example of FIG. 16,a vertex 3010 has a function of communicating with another device 3011present within the vicinity range 3030. The device present in thevicinity range 3030 corresponds to a vertex connected by an edge asillustrated in the first embodiment. Therefore activities in the realworld themselves constitute an edge. That is, the graph structure datain the first embodiment is rendered unnecessary. In addition, in thepresent third embodiment, the device 3020 will be referred to as avertex.

An example of a processing procedure of an information processing methodin the present third embodiment will next be described. FIG. 17 is aflowchart showing the processing procedure in the information processingmethod according to the present third embodiment. In this case, aninformation element as a unit of information is similar to that definedin the first embodiment.

In this case, after starting the flow, each device 3020 firstinitializes the numbers of information elements of the correspondingvertex (step 3111). As details of the processing of step 3111, thedevice 3020 initializes the numbers of information elements to numbersdetermined in advance. For example, when the numbers determined inadvance for the vertex A are zero for the state u and two for the statev, the device 3020 sets the numbers of information elements assigned tothe vertex A such that the number of information elements (state u) iszero and the number of information elements (state v) is two.Thereafter, the device 3020 starts a process of reception processing instep 3112, and starts a process of transmission processing in step 3113.

Next, the device 3020 in step 3114 starts a process of calculationresult obtainment processing. The device 3020 may perform the process ofthe reception processing (step 3112), the process of the transmissionprocessing (step 3113), and the process of the calculation resultobtainment processing (step 3114) in parallel with each other.

The following description will be made of the process of the receptionprocessing (step 3112) among the above-described processes. A maindifference of the reception processing in the present third embodimentfrom the reception processing in the first embodiment is the addition ofprocessing depending on the passage of a fixed time. FIG. 18 shows aflowchart of the process. After starting the process, the device 3020 instep 3211 determines whether a time determined in advance has passed.When the determination sentence is true (step 3211: Y), the device 3020shifts the processing to step 3212.

In addition, the device 3020 in step 3212 determines whether aninformation element is received from another device. When thedetermination sentence is true (step 3212: Y), the device 3020 updatesthe number of information elements of the own device in step 3213. Theprocess is terminated by a termination interrupt or the like. Inaddition, the processing may be triggered by communication rather thanthe passage of the fixed time.

Concrete description will next be made of the process of thetransmission processing (step 3113) mentioned above. A main differenceof the transmission processing in the present third embodiment from thetransmission processing in the first embodiment is the addition ofprocessing depending on the passage of a fixed time.

FIG. 19 shows a flowchart of the process. After starting the process,the device 3020 in step 3311 determines whether a time determined inadvance has passed. When the determination sentence is true (step 3311:Y), the device 3020 shifts the processing to step 3312. The device 3020in step 3312 communicates with vertices present within theabove-described vicinity range 3030 to obtain the numbers of informationelements retained by the vertices.

Subsequent steps 3313-1 to 3313-2 are loop processing for eachinformation element of the own vertex. In step 3314 in the loopprocessing, the device 3020 calculates the transition probability of theown information element. The step is similar to the processing of step514 in the first embodiment.

Thereafter, the device 3020 in step 3315 compares the transitionprobability calculated in the above-described step 3314 with a thresholdvalue determined in advance. When the determination sentence is true asa result of the comparison (step 3315: Y), the device 3020 advances theprocessing to step 3316. For example, when the threshold value is 0.5,and 0≤Transition Probability<0.5, the device 3020 advances theprocessing to step 3311. When 0.5≤Transition Probability≤1, on the otherhand, the device 3020 advances the processing to step 3316.

The device 3020 in step 3316 selects one vertex from the vertices withinthe vicinity range 330. A method for the selection may be carried out ina random manner, a sequential manner, or the like. Thereafter, thedevice 3020 in step 3317 transmits the data (state) of the owninformation element from the network I/F 3025 to the vertex (device ofthe vertex) selected in the above-described step 3316. In addition, thedevice 3020 updates the number of information elements of the own vertexin step 3318 (subtracts one from the number of information elementsbecause the information element is transmitted). In addition, parametersof these pieces of processing (for example the coefficients of thecalculation equation for the transition probability, the thresholdvalue, the selecting method, and the like) may differ between thevertices.

The process of the calculation result obtainment processing (step 3114)mentioned above will next be described concretely. FIG. 20 shows aflowchart of the calculation result obtainment processing. In this case,after starting the calculation result obtainment processing, the device3020 in step 3411 determines whether there is a request to obtain aresult from the input-output device 3024. When this determination istrue (step 3411: Y), the device 3020 performs step 3412 and step 3413.The steps are respectively similar to step 611 and step 612 of the firstembodiment, and therefore description thereof will be omitted.

The above processing solves the classification problem for the verticesas in the first embodiment. The vertices in the third embodiment aredevices distributed in the real world, that is, the classificationproblem for the devices distributed in the real world can be solvedefficiently without graph structure data being generated.

Fourth Embodiment

A fourth embodiment will next be illustrated as an example of aninformation processing system corresponding to a calculation model inwhich each vertex in the foregoing third embodiment is data, and edgesbetween vertices are continuity of access between pieces of data. Thepresent fourth embodiment provides a method of efficiently arrangingdata necessary for processes in computers when the plurality ofprocesses are processed on the plurality of computers.

Suppose that in an information processing system 4000 illustrated inFIG. 21, a computer 120-1 and a computer 120-2 are connected to eachother by a network 1, that the computer 120-1 processes a process 1, andthat a data area 130-1 of the computer 120-1 stores data blocks 1, 2,and 3. In addition, suppose that the computer 120-2 processes a process2, and that a data area 130-2 of the computer 120-2 stores data blocks4, 5, and 6. In addition, the above-described data blocks store datanecessary for the above-described processes 1 and 2.

FIG. 22 shows an example of a chart 4010 of access to data blocks ofsuch respective processes 1 and 2. In the access chart 4010 illustratedin FIG. 22, data blocks adjacent to each other in a time direction in atime section T are judged to have relation to each other. Specifically,in a case where the process 1 accesses the data block 1 and thencontinues to access the data block 2, the relation between the datablock 1 and the data block 2 is incremented by one.

FIG. 23 shows an example of relations 4020, which are calculated by thusadding up the relations of each process. In a table 4021 in FIG. 23, “2”as a value (4202) of the row of the data block 1 and the column of thedata block 2 indicates that a number of times of access to the datablock 2 which access is made successively after access to the data block1 in the above-described time section T is “2.” In addition, a relationgraph 4023 is obtained when the relation table 4021 in FIG. 23 isrepresented by a graph. The graph 4023 does not indicate edges whosenumbers of times are “0.” This result shows that the data blocks 1, 2,and 6 should preferably be stored in the data area of the processingcomputer of the process 1, that is, the data area (130-1), and that thedata blocks 3, 4, and 5 should preferably be stored in the data area ofthe processing computer of the process 2, that is, the data area(130-2).

FIG. 24 is a schematic diagram of an information processing system 4100in the present fourth embodiment. The diagram of the informationprocessing system 4100 shows the configuration of the data areas of theinformation processing system 4000 in FIG. 21 in more detail. Theinformation processing system 4100 has information element storageregions 1 to 6 (4101-1 to 4101-6) corresponding to respective datablocks in the respective data areas 130-1 and 130-2. The storage regionshave a function of storing one or more information elements.

An information processing method according to the present fourthembodiment will next be described. FIG. 25 is a chart 4200 of access ofeach process. Suppose in the present fourth embodiment that processingis performed on the information element storage regions corresponding torespective data blocks at times of access to the respective data blocks.An example 4300 of the processing is shown in FIG. 26. In this case,when the process 1 accesses the data block 1, information elementsstored in the information element storage region 1 are obtained asprocessing 4201 on the region 1. That is, the number of informationelements is subtracted. In the example, the number of informationelements is 10 before the processing, whereas five information elementsstored in the above-described region 1 are obtained in the processing4210 (the number of information elements is 10−5=5). Thereafter, theprocess 1 accesses the data block 2, and thus performs processing 4202on the information element storage region 2 corresponding to theabove-described data block. In the present processing, the fiveinformation elements obtained in the preprocessing 4201 are added to theregion 2 (4211). Then, the processing further obtains an informationelement from the region (4212). Information elements are circulatedbetween the storage regions corresponding to the respective data blocksby repeating such processing. Data blocks having high degrees ofrelation (data blocks that tend to be accessed successively) areclassified into a same cluster according to the distribution ofinformation elements. Data blocks having high degrees of relation can begathered in a same computer when data blocks are moved between dataareas of computers periodically according to the distribution ofinformation elements.

An example of calculation of the present fourth embodiment will beillustrated next. FIG. 27 shows temporal changes in the numbers ofinformation elements stored in the respective information elementstorage regions 1 to 6. In addition, a table 4310 in FIG. 27 shows aninitial state of the numbers of information elements stored in therespective information element storage regions. In the present example,the number of states of an information element is two (the state u andthe state v), and the numbers of information elements that make theabove-described temporal changes and which are shown in the table 4310are calculated by an equation: the number of information elements in thestate u minus the number of information elements in the state v. Inaddition, suppose that in the initial state, the number of informationelements u is 10 and the number of information elements v is zero (thenumber of information elements u minus the number of informationelements v is 10) in the regions 1 to 3 in the data area 130-1, and thenumber of information elements u is zero and the number of informationelements v is 10 (the number of information elements u minus the numberof information elements v is −10) in the regions 4 to 6 in the data area130-2. The number of information elements in each of the regions isupdated each time the data block is accessed, and temporally changes asin FIG. 27.

Directing attention to time Tp in FIG. 27, the magnitude relationbetween the numbers of states of information elements in the storageregion 3 and the storage region 6 is inverted. Therefore, a cluster ofthe regions 1 to 3 and a cluster of the regions 4 to 6 in the initialstate change into a cluster of the regions 1, 2, and 6 and a cluster ofthe regions 3, 4, and 5 at time Tf. This represents a desirable datablock arrangement described above with regard to the relations 4020between the data blocks in FIG. 23.

Fifth Embodiment

An information processing method that is related to baggage pickup workin a warehouse and which deals with a problem of rearranging rackswithin the warehouse so as to shorten the distance of a line of flow ofa worker performing the work will next be illustrated as a fifthembodiment. FIG. 28 illustrates a configuration of a warehouse 500 ofinterest. The warehouse 500 has a plurality of regions. The warehouse500 in FIG. 28 has four regions A (5010-1) to D (5010-4). A plurality ofracks are arranged within each of these regions A to D. In the exampleof FIG. 28, a rack 5011-A1 is shown as one rack placed in the region A(5010-A). However, suppose that a plurality of other racks are arrangedin the region A (5010-A). As with the region A, the regions B to D areprovided with a plurality of racks. Further, a plurality of pieces ofbaggage can be placed in each rack. In the example of FIG. 28, pieces ofbaggage 5012-A1-1 and 5012-A1-2 are shown arranged in the rack A-1(5011-A1). A plurality of pieces of baggage can be similarly arranged inthe other racks.

The pickup work for the baggage arranged in each of the above-describedracks will next be described. FIG. 29 shows an example of the concept ofthe pickup work. In FIG. 29, a worker 5100 picks up pieces of baggageplaced in the respective racks according to a predetermined baggage list5110. In the present example, the pieces of baggage to be picked up arepieces of baggage 5012-A1-1, 5012-B1-2, and 5012-D1-1. The worker 5100therefore goes to the racks 5011-A1, 5011-B1, and 5011-D1 in which therespective pieces of baggage are placed. In that case, a path ofmovement of the worker is a path of movement 5120, for example. In thiscase, order of the pickup of the baggage is not specified.

Description will next be made of a method that, in a case where thereare a plurality of workers in the above-described baggage pickup work,reduces paths of movement of the workers by the information processingmethod according to the present embodiment. FIG. 30 shows a computerprovided to a rack and a mobile terminal retained by a worker. In thepresent example, a computer 5210 is installed on each rack, and eachworker 5100 retains a mobile terminal 5220. FIG. 31 shows an example ofconfiguration of such a computer 5210 and such a mobile terminal 5220.

Description will next be made of the concept of a flow in theinformation processing method in the present example. FIG. 32 shows anexample of a series of pieces of work in which after a state 1 (5301),in which the worker 5100 obtains baggage from the rack B-1 (5011-B1), astate 2 (5302) occurs when the above-described worker 5100 moves fromthe rack B-1 (5011-B1) to the rack D-1 (5011-D1), and thereafter a state3 (5303) is reached when the worker 5100 obtains baggage from the rackD-1 (5011-D1).

When the worker 5100 obtains baggage from the rack B-1 in theabove-described state 1 (5301), communication is performed between themobile terminal 5220 retained by the worker 5100 and the computer5210-B1 provided to the rack B-1 to update the numbers of informationelements of each other. In addition, when the worker 5100 in the state 2moves from the rack, and obtains baggage from the different rack 5011-D1in the state 3, communication is performed between the mobile terminal5220 retained by the worker 5100 and the computer 5210-D1 provided tothe rack D-1 to update the numbers of information elements of eachother. That is, information elements move from the rack B-1 to the rackD-1 via the worker 5100. Information elements are circulated betweenracks when each of the plurality of workers performs such operation.

The computer 5210 of each rack thereafter calculates the movementdestination of the rack from the numbers of information elementsprovided to the computer itself. For example, in a case where there aretwo kinds of information elements (the state u and the state v), aregion as the movement destination is A when the number of informationelements in the state u is larger than the number of informationelements in the state v, and the region as the movement destination is Bwhen the number of information elements in the state u is equal to orsmaller than the number of information elements in the state v. FIG. 33shows a table 5400 describing relation between the computers 5210provided to the respective racks and the movement destinations. Fromthis table 5400, the movement destination of the rack A-1 and the rackB-2 are the region B, and the movement destination of the rack A-2, therack B-1, and the rack B-3 are the region A. Thereafter, each rack movesaccording to the above-described movement destination region. Thismovement is performed by a self-propelled mechanism of the rackreceiving an instruction from the computer 5210 or a robot that movesthe rack. In addition, timing of performing such movement may be in thenighttime every day or may be on alternate days or the like according toan instruction from the computer 5210.

A flowchart of the computer 5210 in the present example will next bedescribed. FIG. 34 is a flowchart of each of the computers 5210. Afterstarting the flow, each computer 5210 initializes the numbers ofinformation elements (step 5511). Thereafter, processes of receptionprocessing (step 5512) and transmission processing (step 5513) of thecomputer 5210 are started. The computer 5210 then starts calculationresult obtainment processing (step 5514). Such pieces of processing(steps 5511 to 5513) can be made similar to the pieces of processing(steps 3111 to 3114) described in FIG. 17 related to the thirdembodiment.

FIG. 35 is a flowchart related to the calculation result obtainmentprocessing (step 5514) among these pieces of processing. In step 5611 inthe flow, each computer 5210 compares the numbers of informationelements in the respective states, which numbers are retained by thecomputer itself, with each other, and selects the state that has amaximum number of information elements. For example, in a case where thenumber of states is two (u and v), and the number of informationelements in the state u on a certain vertex is one and the number ofinformation elements in the state v on the vertex is two, the computer5210 selects the state v. The computer 5210 in step 5612 then determinesthe movement destination region corresponding to the state selected instep 5611 described above, and displays the movement destination regionon a predetermined display device connected by a network or the like orthe input-output device 1024 of the computer itself. For example, whenthe state v is the state of the maximum, the corresponding region is theregion B (the state u is the region A).

The effect of the present fifth embodiment is as shown in FIG. 36. FIG.36 shows an effect of reducing the movement distance of workers by asimulation of the above-described rack movement by the computers 5210.In the present example, supposing that the movement distance in aninitial state 5700 is one, in a converged state 5701 in a case whererack movement based on a result of analysis according to the presentfifth embodiment was performed, the movement distance of workersperforming the pickup work in the warehouse was 0.08, and the reducingeffect was 92%.

Sixth Embodiment

Description will next be made of an information processing method as asixth embodiment in dealing with a problem of user clustering, that is,community detection in interaction service between a plurality of users,such as social network service or the like. In this case, an informationprocessing system is assumed in which each vertex in the foregoing thirdembodiment is a user and edges between vertices are interaction betweenthe users. In this case, the interaction corresponds to for example thetransmission and reception of email, the transmission and reception ofmessages, the visiting of individual pages, posting, and the like.

FIG. 37 shows an example of the concept of the present sixth embodiment.Suppose in FIG. 37 that a user 1 (6011) and a user 2 (6012) userespective terminals 6031 and 6032 possessed by the respective users,and that message transmission (6020) is performed through theinteraction service. In this case, the user 1 transmits a message from amessage transmitting screen 6040, for example, to the user 2 byoperating the terminal 1 (6031) possessed by the user 1. This message isdelivered to the terminal 2 of the user 2 (6032) via an informationprocessing system of the interaction service such as a server or thelike. A message receiving screen 6041, for example, is then displayed onthe terminal.

FIG. 38 shows an example of exchange of information elements in thepresent sixth embodiment. Suppose in FIG. 38 that the informationelements are stored in the terminals retained by the respective users orstorage areas for the respective users in information processing serviceproviding the above-described interaction service. Suppose that aninitial state in this case is a state 1 (6101). When the user 1 (6011)then transmits the message to the user 2 (6012), the terminals 6031 and6032 add information elements to the message. An example of a number ofinformation elements 6113 added to the message by the terminals 6031 and6032 is shown in a state 2 (6102). In this example, five informationelements are added from the terminal 6031 of the user 1.

When the terminal 6032 of the user 2 thereafter receives theabove-described message in a state 3 (6103), a number of informationelements 6112 retained by the terminal 6032 of the user 2 is updated onthe basis of the information elements added to the above-describedmessage. The series of procedures described above causes informationelements to circulate between users via messages. As shown in the thirdembodiment and FIG. 39, communities to which the respective users(=vertices) belong are identified from the thus updated numbers ofinformation elements of the respective vertices (state at time t+2 inFIG. 39). There may be an application for the thus identifiedcommunities in which application for example a keyword appearingfrequently is extracted from text information disclosed by usersbelonging to a same community, and marketing is performed directed tothe users belonging to the community. From the above, the presentembodiment can detect communities of users on the interaction service.

Seventh Embodiment

A mode will next be illustrated as a seventh embodiment, the modesimulating an optimum arrangement of racks in the warehouse illustratedin the foregoing fifth embodiment in a case where the baggage list 5110of each worker 5100 in FIG. 29 can be obtained in advance. In theforegoing fifth embodiment, an optimum arrangement of racks is performedaccording to the movement of the worker 5100 within the warehouse. Inthe present seventh embodiment, on the other hand, when there is asufficient time from the obtainment of the baggage list 5110 by thecomputers 5210 to the shipment of the baggage in the baggage list 5110,for example, the computers 5210 generate graph structure data from thebaggage list 5110, and perform calculation similar to that of theinformation processing system 100 illustrated in the first embodiment.

The computers 5210 calculate the movement destinations of the racks froma result of the calculation (classification result) on the basis of thetable of relation between classification results (numbers of informationelements) and the movement destination regions, which table has beenillustrated in the foregoing fifth embodiment. After such calculation ofthe movement destinations of the racks by the computers 5210, the racksare arranged according to the movement destinations of the rackscalculated by the computers 5210 before the worker 5100 actually startsthe baggage pickup work within the warehouse. Means for arranging theracks is similar to that of the fifth embodiment. Hence, the presentseventh embodiment corresponds to a method in which the computers 5210generate graph structure data from one or more baggage lists 5110.

Description will be made of a concept of the computers 5210 generatinggraph structure data from the above-described baggage list. FIG. 40shows a conceptual diagram of the seventh embodiment. Suppose in thiscase, for example, that there are three baggage lists 7001, 7002, and7003, and that the worker 5100 picks up baggage in numerical order ofthese lists 7001 to 7003. In addition, supposing that each rack is avertex, the trajectory of movement of the worker 5100 within thewarehouse can be expressed as in a graph 7010 of the trajectory of theworker 5100. A value added to each edge in the graph 7010 is a number oftimes of passage of the worker. The normalization of the edges in thegraph 7010 by a maximum number of the above-described numbers of timesof passage provides a normalized graph 7011 of the trajectory of theworker, for example. This graph 7011 represents graph structure datawith a weight added to each edge.

The vertices, that is, the racks can be classified when an informationprocessing system similar to those of the first and second embodimentsprocesses such graph structure data. The movement destination of eachrack can be calculated by calculating the movement destinationscorresponding to a result of the classification by the method accordingto the fifth embodiment.

Eighth Embodiment

A mode in which a computer of the information processing system has thetransition probability Equation (Mathematical Equation 1) in the firstembodiment as a table will next be illustrated as an eighth embodiment.FIGS. 41 to 43 show respective examples of transition probability tables8001 to 8003 as transition probability tables 1 to 3. The computer ofthe information processing system can determine the transitionprobability by checking the numbers of information elements (u and v) ofthe own vertex and the numbers of information elements of the adjacentvertices (ΣNju and ΣNjv) against the transition probability tables 8001to 8003, and identifying a corresponding value in the tables. Values inthe above-described tables 8001 to 8003 are obtained when the computerperforms simulation in advance, and empirically calculates the valuesproviding an intended result.

As described above, according to the information processing system andthe information processing method in accordance with the presentembodiment, it is possible to perform efficient calculation onlarge-scale data that cannot be collected at one place or data updatedmomently.

The description of the present specification clarifies at least thefollowing. In the information processing system according to the presentembodiment, each of the computers may determine, as the attribute of thecorresponding vertex, the attribute of a largest number of pieces ofidentifier data in the identifier data retained by the computer itselfin relation to the vertex. According to this, events corresponding tothe respective vertices can be clustered efficiently.

In addition, in the above-described information processing system, eachof the computers may retain, as the algorithm, a mathematical equationfor calculating the transition probability of the identifier data fromthe own computer to the other adjacent computer by a predeterminedfunction having, as variables, the number of pieces of the identifierdata related to each of the vertices retained by the computer itself andthe number of pieces of the identifier data related to each of thevertices retained by the adjacent computer, and calculate the transitionprobability using the mathematical equation. According to this,efficient and accurate update processing can be performed for thenumbers of pieces of the identifier data as a basis for the clusteringof events.

In addition, in the above-described information processing system, eachof the computers may retain, as the algorithm, a table defining thetransition probability of the identifier data from the own computer tothe other adjacent computer, the table being determined in advanceaccording to relation between the number of pieces of the identifierdata related to each of the vertices retained by the computer itself andthe number of pieces of the identifier data related to each of thevertices retained by the adjacent computer, and calculate the transitionprobability using the table. According to this, more efficient andaccurate update processing can be performed for the numbers of pieces ofthe identifier data as a basis for the clustering of events.

In addition, in the above-described information processing system, whenupdating the numbers of pieces of the identifier data according to theresult of the calculation of the transition probability, each of thecomputers may perform the updating so as to maintain a sum total of thenumbers of pieces of the identifier data retained by the computer itselfand the adjacent computer. According to this,

In addition, in the above-described information processing system, onecomputer may correspond to a plurality of vertices in the graphstructure. According to this, the information processing methodaccording to the present invention can be performed in a server devicethat has control over a plurality of vertices, that is, a plurality ofevents.

In addition, in an information processing system in the presentembodiment, the information processing system including a plurality ofterminals configured to transmit and receive a message via a network anda managing computer, when terminals come into physical proximity to eachother, the terminals may perform direct communication with each other inplace of the transmission and reception of the message via the network,and update the numbers of pieces of the identifier data retained by theterminals. According to this, processing can be performed so as tosupport modes of sending and receiving messages by not only wide-areacommunication lines such as the Internet or the like but also means ofvarious kinds of proximity radio communications or the like.

In addition, in the information processing method according to thepresent embodiment, each of the computers may determine, as theattribute of the corresponding vertex, the attribute of a largest numberof pieces of identifier data in the identifier data retained by thecomputer itself in relation to the vertex.

In addition, in the above-described information processing method, eachof the computers may retain, as the algorithm, a mathematical equationfor calculating the transition probability of the identifier data fromthe own computer to the other adjacent computer by a predeterminedfunction having, as variables, the number of pieces of the identifierdata related to each of the vertices retained by the computer itself andthe number of pieces of the identifier data related to each of thevertices retained by the adjacent computer, and calculate the transitionprobability using the mathematical equation.

In addition, in the above-described information processing method, eachof the computers may retain, as the algorithm, a table defining thetransition probability of the identifier data from the own computer tothe other adjacent computer, the table being determined in advanceaccording to relation between the number of pieces of the identifierdata related to each of the vertices retained by the computer itself andthe number of pieces of the identifier data related to each of thevertices retained by the adjacent computer, and calculate the transitionprobability using the table.

DESCRIPTION OF REFERENCE CHARACTERS

-   1: Network-   100: Information processing system-   220: Computer-   221: CPU-   222: Main storage device-   223: Storage-   225: Network interface-   226: Program-   3020: Device-   5011: Rack-   5220: Mobile terminal

The invention claimed is:
 1. An information processing methodcomprising: arranging a plurality of computers according to a graphstructure as a model, the graph structure being formed by a plurality ofvertices corresponding to events as objects of analysis and each of theplurality of computers having an edge connecting each two adjacentvertices of said plurality of vertices to each other according to arelation between corresponding events, each of the plurality ofcomputers corresponding to a respective vertex of the plurality ofvertices of said graph structure, each adjacent pair of the plurality ofcomputers being connected to each other so as to be able to send andreceive data in correspondence with the edge, and retaining identifierdata having an attribute indicating one or more states for the eventsassociated with the vertices, calculating a transition probability ofidentifier data between each of the pairs of computers corresponding tothe edge connected the adjacent vertices, by a predetermined algorithmbased on numbers of pieces of the identifier data retained by the mutualcomputers, and updating the numbers of pieces of the identifier dataretained by the mutual computers according to a result of thecalculation; and displaying, on a display device, the number of piecesof the identifier data retained in relation to each of the vertices as aspace distribution diagram based on distribution of the vertices,wherein said calculating said transition probability converges to a sameresult regardless of a calculation order among said plurality ofcomputers each corresponding to a respective vertex of the plurality ofvertices of said graph structure.
 2. The information processing methodaccording to claim 1, wherein each of the computers determines, as theattribute of the corresponding vertex, the attribute of a largest numberof pieces of identifier data in the identifier data retained by thecomputer itself in relation to the vertex.
 3. The information processingmethod according to claim 1, wherein each of the computers retains, asthe algorithm, a mathematical equation for calculating the transitionprobability of the identifier data from said one computer to said otheradjacent computer by a predetermined function having, as variables, thenumber of pieces of the identifier data related to each of the verticesretained by the computer itself and the number of pieces of theidentifier data related to each of the vertices retained by the adjacentcomputer, and calculates the transition probability using themathematical equation.
 4. The information processing method according toclaim 1, wherein each of the computers retains, as the algorithm, atable defining the transition probability of the identifier data fromsaid one computer to said other adjacent computer, the table beingdetermined in advance according to relation between the number of piecesof the identifier data related to each of the vertices retained by thecomputer itself and the number of pieces of the identifier data relatedto each of the vertices retained by the adjacent computer, andcalculates the transition probability using the table.
 5. An informationprocessing system comprising: a plurality of computers arrangedaccording to a graph structure as a model, the graph structure beingformed by a plurality of vertices corresponding to events as objects ofanalysis and each of the plurality of computers having an edgeconnecting each two adjacent vertices of said plurality of vertices toeach other according to a relation between corresponding events, each ofthe plurality of computers corresponding to a respective vertex of theplurality of vertices of said graph structure and each adjacent pair ofthe plurality of computers being connected to each other so as to beable to send and receive data in correspondence with the edge; a storagedevice configured to retain identifier data having an attributeindicating one or more states for each of the events associated with oneor more of the plurality of vertices, the identifier data beingaccumulated in each computer; and a display device configured to displaya number of pieces of the identifier data retained in the storage devicein relation to each of the vertices as a space distribution diagrambased on distribution of the vertices; each of the computers calculatinga transition probability of identifier data between one said computerand another one of said computers corresponding to an adjacent vertexconnected by the edge, by a predetermined algorithm based on the numbersof pieces of the identifier data retained by the mutual computers, andupdating the numbers of pieces of the identifier data retained by themutual computers according to a result of the calculation, wherein saidcalculating of said transition probability converges to a same resultregardless of a calculation order among said plurality of computers eachcorresponding to a respective vertex of the plurality of vertices ofsaid graph structure.
 6. The information processing system according toclaim 5, wherein each of the computers determines, as the attribute ofthe corresponding vertex, the attribute of a largest number of pieces ofidentifier data in the identifier data retained by the computer itselfin relation to the vertex.
 7. The information processing systemaccording to claim 5, wherein each of the computers retains, as thealgorithm, a mathematical equation for calculating the transitionprobability of the identifier data from said one computer to said otheradjacent computer by a predetermined function having, as variables, thenumber of pieces of the identifier data related to each of the verticesretained by the computer itself and the number of pieces of theidentifier data related to each of the vertices retained by the adjacentcomputer, and calculates the transition probability using themathematical equation.
 8. The information processing system according toclaim 5, wherein each of the computers retains, as the algorithm, atable defining the transition probability of the identifier data fromsaid one computer to said other adjacent computer, the table beingdetermined in advance according to relation between the number of piecesof the identifier data related to each of the vertices retained by thecomputer itself and the number of pieces of the identifier data relatedto each of the vertices retained by the adjacent computer, andcalculates the transition probability using the table.
 9. Theinformation processing system according to claim 5, wherein whenupdating the numbers of pieces of the identifier data according to theresult of the calculation of the transition probability, each of thecomputers performs the updating so as to maintain a sum total of thenumbers of pieces of the identifier data retained by the computer itselfand the adjacent computer.
 10. The information processing systemaccording to claim 5, wherein one computer corresponds to a plurality ofvertices in the graph structure.
 11. An information processing systemcomprising: a managing computer; computers installed on respective rackswithin a warehouse; and portable terminals carried by respective workerscollecting baggage arranged in the racks, the portable terminals beingable to access the computers; each of the computers retaininginformation on numbers of pieces of identifier data of a plurality ofkinds of information elements corresponding to respective predeterminedevents related to the racks or the baggage arranged in the correspondingracks, and receiving identifier data retained by each of the portableterminals when accessed by the portable terminal, transmittingidentifier data determined on a basis of conditions of past changes inthe identifier data already retained by the computer to the portableterminal, and updating the numbers of pieces of the identifier data ofthe plurality of kinds of information elements, the identifier data ofthe plurality of kinds of information elements being retained by thecomputer, on a basis of a subtracted number as a result of thetransmission and reception of the identifier data to and from theportable terminal, each of the portable terminals receiving theidentifier data from the computer, and updating identifier data retainedby the portable terminal itself on a basis of the received identifierdata, wherein said updating of the identifier data retained by theportable terminal is performed independently of an order of saidreceiving of said identifier data from each of the portable terminals bysaid managing computer, and each of the computers outputting, to themanaging computer, an instruction to move the rack on which the computeritself is installed to an arrangement destination associated with a kindcorresponding to a largest number of the numbers of pieces of theidentifier data of the plurality of kinds of information elements, theidentifier data of the plurality of kinds of information elements beingretained by the computer itself, after passage of a predetermined timefrom the updating of the numbers of pieces of the identifier data. 12.An information processing system comprising: a managing computer; andcomputers associated with respective pieces of data within a datacenter; each of the computers retaining information on numbers of piecesof identifier data of a plurality of kinds of information elementscorresponding to respective predetermined events related to the data,and when accessed by programs using each of the pieces of data,receiving identifier data retained by the programs, giving the programsidentifier data determined on a basis of conditions of past changes inthe identifier data already retained by the computer, and updating thenumbers of pieces of the identifier data of the plurality of kinds ofinformation elements, the identifier data of the plurality of kinds ofinformation elements being retained by the computer, on a basis of asubtracted number as a result of the transmission and reception of theidentifier data to and from the programs, each of the programs receivingthe identifier data from the computer, and updating identifier dataretained by the program itself on a basis of the received identifierdata, and each of the computers outputting, to the managing computer, aninstruction to move the data with which the computer itself isassociated to an arrangement destination associated with a kindcorresponding to a largest number of the numbers of pieces of theidentifier data of the plurality of kinds of information elements, theidentifier data of the plurality of kinds of information elements beingretained by the computer itself, after passage of a predetermined timefrom the updating of the numbers of pieces of the identifier data,wherein said updating identifier data retained by the program isperformed independently of an order of said receiving of said identifierdata from each of the programs.
 13. An information processing systemcomprising: a plurality of terminals configured to transmit and receivea message via a network; and a managing computer; each of the terminalsretaining information on numbers of pieces of identifier data of aplurality of kinds of information elements, when transmitting themessage, transmitting, to another terminal among the plurality ofterminals, the message to which identifier data determined on a basis ofconditions of past changes in the numbers of pieces of identifier dataalready retained by the terminal itself is added, and updating thenumbers of pieces of the identifier data of the plurality of kinds ofinformation elements, the identifier data of the plurality of kinds ofinformation elements being retained by the terminal itself, bysubtracting a number of pieces of the transmitted identifier data, whenreceiving the message, updating the numbers of pieces of the identifierdata of the plurality of kinds of information elements, the identifierdata of the plurality of kinds of information elements being retained bythe terminal itself, by adding a number of pieces of identifier dataadded to the received message, and transmitting, to the managingcomputer, the numbers of pieces of the identifier data of the pluralityof kinds of information elements, the identifier data of the pluralityof kinds of information elements being retained by the terminal itself,after passage of a predetermined time from the updating of the numbersof pieces of the identifier data, wherein said updating of the number ofpieces of the identifier data is performed independently of an order ofsaid receiving of said identifier data from each of the terminals bysaid managing computer, and the managing computer receiving the numbersof pieces of the identifier data of the plurality of kinds ofinformation elements from each of the terminals, and displayinginformation indicating that terminals each having a largest number ofpieces of identifier data belonging to a common kind of informationelement are in a same group on a display terminal of the managingcomputer.
 14. The information processing system according to claim 13,wherein when terminals come into physical proximity to each other, theterminals perform direct communication with each other in place of thetransmission and reception of the message via the network, and updatethe numbers of pieces of the identifier data retained by the terminals.